A Model-Fitting Approach To Cluster Validation With Application To Stochastic Model-Based Image Segmentation

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Abstract
An unsupervised stochastic model-based image segmentation technique requires the model parameters for the various image classes in an observed image to be estimated directly from the image. In this work, a clustering scheme is used for the model parameter estimation. Most of the existing clustering procedures require prior knowledge of the number of classes which is often, as in unsupervised image segmentation, unavailable and has to be estimated. The problem of determining the number of classes directly from observed data is known as the cluster validation problem. For unsupervised image segmentation, the solution of this problem directly affects the quality of the segmentation. In this work, we propose a model-fitting approach to the cluster validation problem based upon Akaike's Information Criterion (AIC). The explicit evaluation of the AIC is achieved through an approximate maximum-likelihood (ML) estimation algorithm. We demonstrate the efficacy and robustness of the proposed approach through experimental results for both synthetic mixture data and image data.

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